Project-Based Learning: A Case Study of Early Data Analytics Learning in Undergraduate Mathematics
Notice bibliographique
Résumé
The overall goal of this study was to examine the design and impact of project-based learning (PBL) for early data analytics learning in undergraduate mathematics at a four-year research university in the United States. I documented the plans, activities and participant feedback gathered as part of the original funded program at the university, and delved further into the archival data to explore the feasibility and impact of introducing data analytics learning early in the undergraduate mathematics curriculum. The relationships between student development of knowledge, ability, and confidence during this PBL-infused program were also examined, and the benefits and challenges from the perspectives of the learner and the teacher were detailed.The study provided a successful example of how a PBL-infused approach could be used to effectively teach data analytics early in the mathematics undergraduate curriculum. The findings suggested that the early introduction to data analytics program resulted in additional learning, research and internship opportunities, and informed students’ undergraduate studies, choice of major, and other life choices such as plans for graduate studies and/or careers. Interactions and collaborations in visualizing problem solving in the context of projects using real-world data afforded the students additional opportunities to network with their peers, faculty, and university, industry, and/or community partners. This created a community of learners with shared goals and achievements. These collaborations and interactions supported students’ development of knowledge and ability, both of which were positively correlated with confidence in this early data analytics experience. This study illuminates the design features of the early data analytics learning and research experience, and offers a refined design framework for PBL-infused data analytics curricula in mathematics. The refined design framework includes: real-world content for greater accessibility, visualization of the problem solving, incorporating interactions and collaborations among all in the community of learners, and the promotion of knowledge, confidence and beliefs in support of lifelong learning. The potential of the case study and the refined framework to transform learning across STEM disciplines bolsters the potential broader impact.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».